B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide

Research/Writer: Karen Burka, Senior Research Consultant
Editor: Claire Schoen, VP, Marketing Services, Third Door Media
Editorial Advisor: David Raab, Principal, Raab Associates

Editor’s Note: The following is an excerpt from "B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide." You can download the report here.

What is Predictive Marketing Analytics?

For the purposes of this buyer’s guide, we use the following definition: software that uses machine learning and statistical models to forecast outcomes and trends based on existing or similar historical customer data. The data can come from multiple sources and may be demographic, firmographic, and behavior based (i.e., purchases, interests, form fills). The goal is to enable businesses to predict which leads and/or accounts are most likely to convert and/or have the biggest revenue impact.

This guide focuses on the lead discovery, scoring, and enrichment category of predictive marketing analytics vendors. Companies that license sales advisory or customer retention tools or platforms are beyond the scope of this report.

Predictive marketing analytics use cases

There are a variety of ways that B2B marketers can use predictive marketing analytics platforms, including (but not limited to):

  • Generating net-new leads (i.e., prospecting): Marketers can use predictive modeling technology to sort through thousands of firmographics (i.e., company size, revenue, purchases) and signals (i.e., business expansion, new job posts, management changes) to determine which are good indicators of future behavior and then use them to identify lookalike prospects not currently in the company’s database.
  • Lead prioritization: Marketing and sales can use lead scores to prioritize known prospects and estimate their likelihood of taking a desired action. The predicted action is usually purchase, but could be another measure such as lifetime revenue, profitability, promotion response, or sales acceptance. Model-based scoring is almost always more accurate than manually built scoring formulas.
  • Account-based marketing: Using identity association features, marketers can establish account/company hierarchies, division relationships, and purchase decision-makers to initiate sales at the account level. This approach allows sales organizations to better coordinate their efforts.
  • Persona and customer segment building: Marketing and sales departments can use models to place individuals into groups, personas, and/or sales stages based on company characteristics, personal interests, motivators or role in the purchase process. These segmentations can be used to assign leads to campaigns, select marketing approaches or product families, and guide lead scoring. Segments such as “persona” are largely static, while others such as “sales stage” or “engagement level” can change over time. Model driven segmentation utilizes more data, and can surface subtle relationships among data elements.
  • Cross-selling and upselling existing customers: Marketing and sales staffs can use the intelligence provided by predictive models and lead scores to target contacts and companies that offer the highest potential for cross-sell and upsell opportunities.
  • Sales enablement: Through lead enrichment, scoring, and routing, sales reps can better prioritize leads and accounts and improve productivity. Intent data and alerts can also provide sales staff with the ability to be more proactive and timely with their outreach. Predictive models can help to identify the customers most at risk for not renewing their contracts and go a step further to identify which customers are likely to respond to special renewal incentives.

Do you need a B2B predictive marketing analytics platform?

Deciding whether or not your company needs a B2B predictive marketing analytics platform calls for the same evaluative steps involved in any software adoption, including a comprehensive self-assessment of your organization’s business needs, staff capabilities, management support, and financial resources. Use the following questions as a guideline to determine the answers.

  • Do we have gaps in our data that are preventing us from understanding which markets our company should address? Are your sales reps hitting their goals? Do you know which accounts to engage in order to prevent churn and increase revenue? If not, then perhaps predictive marketing analytics can help.
  • Do we have marketing automation and CRM systems already installed, and do we use them consistently across the sales and marketing organizations? To recognize value from predictive marketing analytics, scalable engagement and tracking systems must be in place to capture and capitalize on the predictive system’s deliverables, which can include lead scores, enriched lead data, and net-new leads.
  • Are we ready to mine all of our data assets to find predictive insights? A Forrester global survey on data and analytics found that most B2B companies use only a fraction of their internal data. Can marketing work with information technology (IT) to harvest data lying dormant in your operational systems and use predictive analytics to reveal, for instance, how loyal customers differ from those who buy once and are never heard from again? If not, collaborate to develop a technology infrastructure that will harness predictive insights continuously and turn customer data into action.
  • Do we have staff and financial resources to allocate to the predictive marketing system? You don’t need a data scientist on staff to use the models or lead scores provided by a predictive marketing analytics platform. But you will want your marketing resources to use the insights uncovered by the platform to pursue growth opportunities.
  • Can we demonstrate ROI for a specific task to build C-level/executive buy in? For most organizations, the best predictive analytics starting point is lead scoring and building ideal customer profiles for target account selection. Do you have a clearly defined profile? Does your current lead scoring system predict which leads will close at the highest rate? If you can demonstrate success in one of these two areas with a pilot program, you can win C-suite support for the predictive marketing analytics investment.

Learn More!

Download “B2B Predictive Marketing Analytics” The 35-page PDF includes additional valuable information including:

  • Predictive marketing analytics market overview and trends.
  • In-depth analysis of platform capabilities and features.
  • Profiles of 13 leading vendors.
  • Specific questions to ask during a platform demo.
  • …and much more
B2B Predictive Marketing Analytics Platforms A Marketer's Guide

Companies Profiled in this report:

  • 6sense
  • EverString
  • GrowthIntel
  • Infer
  • Lattice Engines
  • Leadspace
  • Mintigo
  • Radius
  • SalesPredict

Cloud solutions with predictive capabilities:

  • Adobe
  • IBM
  • Oracle
  • Salesforce